A Unifying Framework for Global Optimization: From Theory to Formalization

Gaëtan Serré; Argyris Kalogeratos; Nicolas Vayatis

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arXiv · 2508.20671

A Unifying Framework for Global Optimization: From Theory to Formalization

Gaëtan Serré·Argyris Kalogeratos·Nicolas Vayatis

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Abstract

We introduce an abstract measure___theoretic framework that serves as a tool to rigorously study stochastic iterative global optimization algorithms as a unified class. The framework is formulated in terms of probability kernels, which, via the Ionescu--Tulcea theorem, induce probability measures on the space of sequences of algorithm iterations, endowed with two intuitive properties. This framework answers the need for a general, implementation___independent formalism in the analysis of such algorithms, providing a starting point for formalizing global optimization results in proof-assistants. To illustrate the relevance of our tool, we show that common algorithms fit naturally in the framework, and we also use it to give a rigorous proof of a general consistency theorem for stochastic iterative global optimization algorithms (Proposition 3 of (Malherbe, et al., 2017). This proof and the entire framework are formalized in the Lean proof assistant. This formalization both ensures the correctness of the definitions and proofs, and provides a basis for future machine-assisted formalizations in the field.

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Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis. 2025-08-28. A Unifying Framework for Global Optimization: From Theory to Formalization. https://arxiv.org/abs/2508.20671

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